Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20-120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2-20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

M. C. Dietze et al., “Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis,” JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, vol. 116, 2011.

@article{1981118,
abstract = {Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20-120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2-20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.},
articleno = {G04029},
author = {Dietze, Michael C and Vargas, Rodrigo and Richardson, Andrew D and Stoy, Paul C and Barr, Alan G and Anderson, Ryan S and Arain, M Altaf and Baker, Ian T and Black, T Andrew and Chen, Jing M and Ciais, Philippe and Flanagan, Lawrence B and Gough, Christopher M and Grant, Robert F and Hollinger, David and Izaurralde, R Cesar and Kucharik, Christopher J and Lafleur, Peter and Liu, Shugang and Lokupitiya, Erandathie and Luo, Yiqi and Munger, J William and Peng, Changhui and Poulter, Benjamin and Price, David T and Ricciuto, Daniel M and Riley, William J and Sahoo, Alok Kumar and Schaefer, Kevin and Suyker, Andrew E and Tian, Hanqin and Tonitto, Christina and Verbeeck, Hans and Verma, Shashi B and Wang, Weifeng and Weng, Ensheng},
issn = {0148-0227},
journal = {JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES},
keywords = {TEMPORAL VARIABILITY,WAVELET ANALYSIS,MULTISCALE ANALYSIS,DIOXIDE UPTAKE,EXCHANGE,VEGETATION,CO2,BIOSPHERE,FOREST,UNCERTAINTY},
language = {eng},
pages = {15},
title = {Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis},
url = {http://dx.doi.org/10.1029/2011JG001661},
volume = {116},
year = {2011},
}